The present disclosure relates to methods for image segmentation, and more particularly to a method for multi-atlas based segmentation.
Image segmentation is a process of partitioning an image into one or more groups of pixels sharing certain characteristics. A group or groups of pixels can delineate an object of interest. Image segmentation simplifies the representation of an image and can be performed in two dimensions (2D) or three dimensions 3D.
In the context of medical imaging, accurate delineation of anatomical structures is critical for reliable quantitative analysis. For example, segmentation of left and right ventricles in cardiac images is a prerequisite for assessment of cardiac function (e.g., volume measurements, estimation of ejection fraction and myocardial motion analysis) as well as diagnosis of various cardiac diseases. In clinical routines, the segmentation task is often performed manually, which is tedious and time-consuming, in particular when dealing with very large number of scans, e.g., screening practice. Manual segmentation is also difficult to reproduce and suffers from inter and intra-observer variabilities.